关键词: Yolo_v2, 残差网络, 多尺度, 激活函数, 多方位, 违章检测

Abstract:

In recent years, with the rapid growth of car ownership in China, the traffic pressure and safety hazards caused by cars are increasing year by year, so it is particularly important to supervise the behavior of vehicles on the road. Therefore, an improved Yolo_v2 convolutional neural network is proposed for vehicle detection. Firstly, the structure of Yolo_v2 network is improved, residual network is added to improve the accuracy, and multi-scale layer is added to improve the detection accuracy of different size targets in the image. Secondly, Kelu activation function is designed based on the ELU activation function to further improve the detection accuracy. Thirdly, multi-directional vehicle data set and license plate data set are produced. Finally, the vehicle detection system and vehicle are combined. The license plate detection system is integrated into ROS system and communicates with QT creator visual interface, so as to observe the experimental results more clearly. The test results show that the improved Yolo_v2 convolution neural network has a superior performance in traffic violation monitoring.

Key words: Yolo_v2, residual network, multiscale, activation function, multi-directional, violation monitoring